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Energy Losses in Transformers01:21

Energy Losses in Transformers

902
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
902
Transformers in Distribution System01:27

Transformers in Distribution System

124
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
124
Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K
Types Of Transformers01:16

Types Of Transformers

1.0K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.0K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

176
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
176
Reducing Line Loss01:18

Reducing Line Loss

173
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
173

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相关实验视频

Updated: Jul 19, 2025

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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变压器用于能源预测

Hugo S Oliveira1,2, Helder P Oliveira1,2

  • 1Institute for Systems and Computer Engineering, Technology and Science-INESC TEC, University of Porto, 4200-465 Porto, Portugal.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
概括
此摘要是机器生成的。

准确的建筑能耗预测对于能源效率至关重要. 一个新的变压器模型显著改善了预测,超过了可持续建筑运营的旧方法.

关键词:
时间序列预测预测变压器 变压器

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科学领域:

  • 建筑科学 建筑科学
  • 人工智能的人工智能
  • 能源系统 能源系统

背景情况:

  • 随着能源需求的不断增长和气候变化,建筑物需要提高能源效率.
  • 精确的能源消耗预测是优化建筑性能和运营的关键.
  • 识别能效升级依赖于可靠的消耗预测.

研究的目的:

  • 开发和评估建筑能源消耗的先进预测模型.
  • 为了应对能源使用中的多变量时间序列预测的挑战.
  • 提高能源消耗预测的准确性和稳定性,以优化建筑管理.

主要方法:

  • 针对多变量时间序列分析,提出了一种修改的多头变压器模型.
  • 引入了一个可学习的权重特征注意力矩阵,以结合输入变量.
  • 该模型的性能与经常性神经网络 (RNN) 模型进行了比较.

主要成果:

  • 拟议的基于变压器的模型在预测能源消耗方面表现强.
  • 与RNN模型相比,该模型实现了较低的平均绝对百分比误差.
  • 多变量方法有效地整合了预测的各种输入因素.

结论:

  • 修改后的变压器模型为多变量能耗预测提供了卓越的性能.
  • 这种先进的模型可以集成到未来的系统来追踪能源场景.
  • 这些发现有助于创造更可持续,更节能的建筑使用模式.